AI Solution Design and Model Lifecycle
Designing for the whole lifecycle
Solution design begins with the decision the system supports and the constraints around it: accuracy needed, latency, cost per call, explainability, privacy, and failure tolerance. Candidates should be able to choose among rules, classical ML, pretrained models, fine-tuning, retrieval augmentation, and agentic orchestration - and justify the simplest option that meets the bar. Know when retrieval beats fine-tuning (changing knowledge, traceability needed) and when fine-tuning is warranted (style, format, domain behavior).
Lifecycle stages: problem framing, data preparation, experimentation, evaluation, deployment, monitoring, and retirement. Evaluation deserves emphasis: holdout and out-of-distribution testing, task-specific metrics, human review for generative quality, red-teaming for safety, and online metrics that differ from offline ones. Understand drift types (data, concept, label) and the monitoring signals that catch each.
Traps in questions
- Selecting the most capable model when a smaller, cheaper one meets the accuracy bar.
- Skipping a baseline; without one, improvement claims are unfalsifiable.
- Confusing offline benchmark scores with production performance.
- Treating deployment as the end; models without monitoring and a rollback path are liabilities.
- Ignoring non-determinism in generative systems when designing tests and SLAs.
Rule of thumb
Simplest model that clears the bar, with an evaluation set built before modeling. Define acceptance criteria and a golden dataset first, then iterate. Worked example: a contract-review assistant - start with retrieval over approved clauses plus a capable general model, measure precision/recall against 300 lawyer-labeled examples, add fine-tuning only if formatting or reasoning failures persist, and gate release on a red-team pass and a monitored rollout to 5% of users.